Guide advanced prompt engineering techniques for LLM performance and reliability.
npx skills add https://github.com/wpank/ai --skill prompt-engineering-wpank
Please help me install this Agent Skill. Skill: prompt-engineering Source: https://github.com/wpank/ai/tree/main/skills/writing/prompt-engineering Command: npx skills add https://github.com/wpank/ai --skill prompt-engineering-wpank
This Skill addresses the challenge of getting consistent, high-quality outputs from Large Language Models (LLMs) by providing advanced techniques to design, optimize, and validate prompts.
Use the prompt engineering skill to design a prompt for sentiment analysis with structured JSON output.
High-intent search queries and answers about installing and using this skill.
Chain-of-thought and few-shot learning are advanced prompt engineering techniques covered here to guide LLMs through complex reasoning. This Skill provides patterns to implement these methods, ensuring consistent, high-quality outputs for production applications.
Structured JSON output from an LLM requires specific prompt engineering patterns to enforce schema constraints. This Skill offers guidance on designing prompts for precise data extraction, ensuring the model returns reliable and predictable structured formats.
Reducing token usage and improving LLM response reliability requires prompt optimization strategies. This Skill provides performance optimization patterns that minimize unnecessary tokens while maximizing output controllability and accuracy.
Evaluating LLM prompts for production requires systematic validation methods to measure performance and reliability. This Skill includes evaluation techniques to test your prompts, ensuring they maintain accuracy and efficiency under real-world conditions.
Advanced prompt engineering is highly effective for complex reasoning and data extraction tasks. This Skill supplies practical patterns for designing prompts that ensure accuracy and efficiency when building AI features that require precise outputs.
ResearAIStandardize a reference object for quest evaluation with plan and checklist requirements.
JamesFincherConvert LLM responses into Pydantic-validated JSON with automatic retries.
Marwan78888Design prompts for LLM tasks with structured schemas and evaluation rubrics.
Clay-HHKPrune transformer models with Wanda, SparseGPT, and N:M sparsity patterns.
